Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions

To assess the image quality parameters of dual-energy computed tomography angiography (DECTA) 40-, and 60 keV virtual monoenergetic images (VMIs) combined with deep learning-based image reconstruction model (DLM) and iterative reconstructions (IR). CT scans of 28 post EVAR patients were enrolled. The 60 s delayed phase of DECTA was evaluated. Objective [noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR)] and subjective (overall image quality and endoleak conspicuity – 3 blinded readers assessment) image quality analyses were performed. The following reconstructions were evaluated: VMI 40, 60 keV VMI; IR VMI 40, 60 keV; DLM VMI 40, 60 keV. The noise level of the DLM VMI images was approximately 50% lower than that of VMI reconstruction. The highest CNR and SNR values were measured in VMI DLM images. The mean CNR in endoleak in 40 keV was accounted for as 1.83 ± 1.2; 2.07 ± 2.02; 3.6 ± 3.26 in VMI, VMI IR, and VMI DLM, respectively. The DLM algorithm significantly reduced noise and increased lesion conspicuity, resulting in higher objective and subjective image quality compared to other reconstruction techniques. The application of DLM algorithms to low-energy VMIs significantly enhances the diagnostic value of DECTA in evaluating endoleaks. DLM reconstructions surpass traditional VMIs and IR in terms of image quality.


Objective image quality
The results of the objective image quality assessment of all the evaluated reconstructions are summarized in Table 1. Figure 1 shows an example of ROI positioning.
There are significant differences between the 40-and 60-keV VMI images in all three sets of reconstructions.Differences in noise levels between the VMI and VMI IR reconstruction were not statistically significant.The noise level of the VMI DLM images was roughly 2 × lower than that for VMI reconstructions (VMI 40 keV = 83.96± 23.56; DLM 40 keV VMI = 45.07 ± 12.98).However, slight but statistically significant differences between the SNR and CNR values between VMI and VMI IR 60% were observed.The highest CNR and SNR values were measured for VMI DLM images.The mean CNR in endoleaks in 40 keV was accounted for as 1.83 ± 1.2; 2.07 ± 2.02; 3.6 ± 3.26 in VMI, VMI IR, and VMI DLM, respectively.Figure 2 shows the results of the CNR and SNR calculations for the endoleaks.
An example of endoleak ROI measurements in all assessed reconstructions is shown in Fig. 3.

Subjective image quality
The results of the subjective image quality assessments performed by the three readers are summarized in Table 2 and Fig. 4.
The data in the tables represent the mean ratings of three readers.Overall, the subjective image quality was the lowest for VMI 40 keV (mean rating 3.61 ± 0.63), and this was significantly lower than that of DLM VMI 60 keV and IR 60% VMI 40 keV (p < 0.001).The highest subjective image quality was observed with DLM VMI 60 keV (mean 4.21 ± 0.79).The objective image quality analysis results showed significant differences between the 40-and 60-keV VMI images across all three sets of reconstructions.
The results of the subjective endoleak presence assessment are summarized in Table 3 and Fig. 5.The endoleak presence assessment using a 5-point CS showed the lowest ratings for VMI 60 keV (mean rating 4.25 ± 1.16) and the highest for DLM VMI 40 keV (mean rating 4.56 ± 0.80); the difference was statistically significant (p = 0.012).DLM VMI 40 keV ratings also showed the smallest variability (SD = 0.80).It is worth noting that the results of the endoleak presence certainty analysis revealed very high values across all analyzed reconstructions (4.25-4.56).The inter-reader concordance of the image quality and endoleak conspicuity assessments was fair (Table 4).

Error study
Analysis of the repeatability of subjective image quality analysis carried out by the reader demonstrated excellent concordance (ICC = 0.837).

Discussion
In this study, we aimed to compare the image quality parameters of different postprocessing algorithms for the detection of endoleaks in patients after EVAR.Our findings revealed that DLM-based noise reduction 40 and 60 VMI highly surpasses both subjective and objective image quality of conventional VMIs and IR VMIs.
Our comparison between the VMI and IR VMI images revealed slight but statistically significant differences in CNR, SNR, and noise levels.The results of the subjective image quality assessment showed the highest endoleak conspicuity and overall image quality ratings for DLM reconstructions.The results of the qualitative analysis showed that the highest endoleak conspicuity rating was given to the DLM VMI 60 keV.
Considering the available literature and our own observations, we have made an initial selection of the analyzed VMIs at 40 and 60 keV.The choice of reconstruction presenting low-energy levels of VMIs was dictated by the higher absorption of low-energy photons by iodine.These properties result from the k-edge of iodine (33.2 keV) and constitute the main advantage of VMI angiographic imaging 43 .Previous studies have shown that 40 keV VMIs allow for the highest CT number values in angiographic studies; however, they are burdened with significant noise [44][45][46] .A stepwise decrease in image noise was observed with an increase in VMI energy levels; however, it was accompanied by a significant decrease in CT attenuation, with the lowest values at the highest photon energies 47 .Therefore, a series of studies indicate the usefulness of 60 keV reconstructions in angiographic studies, which on one hand show high contrast attenuation, and on the other hand, acceptable image noise levels, resulting in high CNR and SNR values [48][49][50] .Both chosen reconstructions have already proven high image quality parameters in angiographic studies, as well as in endoleak detection [17][18][19][20][21][22] .In a study by Maturen et al. 17 , 55 keV VMIs had higher endoleak conspicuity ratings compared to VMI 75 keV.Charalombous et al. 22 reported a 54 keV VMI to enhance the endoleak detection efficiency.In a recent study in 2023, Kazimierczak et al. 19 showed a high diagnostic value of 40-keV VMIs in endoleak detection, exceeding LB reconstructions.Our results clearly show that despite lower CNR levels, both subjective and overall image quality and endoleak conspicuity ratings were higher for 60 keV compared to 40 keV reconstructions.
The findings of this study highlight the potential advantages of utilizing low-energy VMI reconstruction combined with advanced reconstruction techniques such as IR and DLM to evaluate endoleaks after f/brEVAR procedures.The objective and subjective image quality parameters were significantly higher for IR and DLM reconstructions than for standard VMIs.As indicated before, low-energy VMIs have great potential for DECTA endoleak detection [17][18][19][20][21] , and noise-optimized reconstructions allow for either improved endoleak conspicuity 18 or significant radiation dose reduction while maintaining image quality 51,52 .Martin et al. 18 investigated the image quality parameters and diagnostic accuracy of VMI and noise-optimized VMI reconstructions for endoleak detection.Both the image quality parameters (CNR) and results of the ROC analysis for endoleak detection were significantly higher in VMI and noise-optimized VMI reconstructions.The literature also indicates numerous successful applications of IR algorithms in low-radiation dose protocols 53,54 .Studies by Hansen et al. and Naidu et al. 51,52 showed a potential 72-73% radiation dose reduction with model-based iterative reconstruction (MBIR), with comparable image quality parameters and preserved diagnostic accuracy.However, despite the promising results, this technique is not widely used because of the prolonged reconstruction time and the artificial, "plastic" appearance of the images 39 .Therefore, it was particularly interesting to compare the image quality parameters of both 40 and 60 keV VMIs reconstructed using the two different noise reduction approaches, iterative and AI-based.
A recent breakthrough in AI technology, resulting in the development of generative AI, has led to the creation of AI-driven noise optimization tools that utilize DLR algorithms, surpassing the capabilities of iterative reconstruction (IR), including MBIR 39 .A few studies have analyzed the performance of the evaluated DLM tool (ClariCT.AI) 41,55,56 .Nam et al. 41 compared the subjective image quality of ClariCT.AI with iterative reconstruction (IR) on 100 low-dose chest CT scans.The noise level, spatial resolution, and overall image quality of ClariCT.AI were superior to those of IR.In a recent study, Seo et al. 55 proved that DLM VMI 40 keV images exhibited greater noise reduction, better lesion conspicuity, enhanced image contrast, and higher overall image quality than IR in patients with hypervascular liver lesions.In a similar study, Lee et al. 56 demonstrated that DLM VMI 40 keV provided better image quality and comparable diagnostic performance in the detection of hypovascular hepatic lesions compared to VMI 40 keV.
Few studies have been conducted on the performance of DLM algorithms for cardiac and vascular CT 32,[57][58][59][60] .In cardiac CT, vessel contouring is adequate with routine-dose FBP, IR, and MBIR, but deteriorates with low-dose scans due to increased noise and reduced spatial resolution.However, DLRs have the potential to overcome the limitations of these techniques 57,58 .In one of the first published studies in this area, Tatsugami et al. 39 demonstrated that DLR reduced image noise and improved the image quality in coronary CTA.Benz et al. 60 found that DLR improved image quality over HIR in coronary CT angiography but did not enhance diagnostic accuracy for stenosis compared to invasive angiography.Bernard et al. 59 reported a 40% dose reduction with DLR compared to IR in cardiac CT angiography (CTDI_vol: 6.9 mGy vs 11.5 mGy), increasing SNR and CNR by 50%.The results of our study are in line with the above-mentioned studies and make a significant contribution to the narrow field of knowledge on the application of AI in noise reduction in CT imaging of the vascular system.
Figure 6 shows a case of extremely high image noise in an obese male patient (BMI = 33.1).Despite significant differences in image noise measurements, all six evaluated reconstructions were qualitatively rated as barely diagnostic; all six were unanimously marked by the readers with two points.This example clearly demonstrates that, despite improvements in objective image quality parameters, the overall subjective image quality might remain unchanged.The 2023 study on the use of metal artifact reduction (MAR) and IR reconstructions for stent visualization in f/bEVAR patients showed that, despite the high objective image quality parameters in MAR reconstructions, the subjective image quality was the worst in these reconstructions 50 .Both studies suggest that while the contrast-to-noise ratio (CNR) has undeniable value in the qualitative assessment of images, it is not the sole indicator determining the diagnostic value of the study.CNR primarily assesses contrast resolution but overlooks other critical aspects such as sharpness and spatial resolution, therefore other are crucial in comprehensive image quality assessment 61 .
The results of this study should be considered within the context of its limitations.First, the patient population was relatively small, yet sufficient for the analyses performed.Second, the results are specific to the DECT acquisition and postprocessing techniques, which are unique to one vendor.An IR with a specified potency was also employed, which affected the results of the image quality assessment.The subjective nature of image quality assessment can be influenced by individual biases.Finally, we only evaluated the DLM-based algorithm.Therefore, the results of this study should be regarded with an awareness of the specific hardware and software solutions applied.

Conclusion
In conclusion, the use of DLM VMI images caused a significant increase in the diagnostic value of the examination due to a substantial increase in both subjective and objective image quality parameters.The use of iterative algorithms and DLM images increased the quantitative image parameters compared with VMI reconstructions and should be considered for inclusion in diagnostic protocols.

Materials and methods
The study was approved by the Ethics Committee of Collegium Medicum, Nicolaus Copernicus University in Torun, Poland.The study was conducted in accordance with the Declaration of Helsinki and relevant guidelines.All patients provided written informed consent.

Population
The study involved 28 consecutive patients who underwent the f/brEVAR procedure and were referred for 28 CTAs performed between August 2019 and December 2020.A follow-up examination was conducted for every patient 1 month after stentgraft implantation procedure.The exclusion criteria were: known severe adverse reactions to iodinated contrast media and impaired renal function (glomerular filtration rate < 30 mL/min).

CT scanning protocol and image reconstruction
All CT scans were obtained using a dual-energy fast-kVp switching scanner (Discovery 750 HD, GE Healthcare, Milwaukee, WI, USA).The standard examination protocol consisted of three phases: one non-enhanced phase and two post-contrast dual-energy acquisitions (arterial and 60 s delayed-phase.Both post-contrast phases were acquired using the following tube parameters: tube voltage 80-140 kV, tube current 360 mAs, pitch 0.985:1, slice thickness 0.625 mm and a 35 cm DFOV.Intravenous administration of 80 mL of iohexol (350 mg I/ml), a nonionic iodine contrast agent, was performed at a rate of 4 mL/min through the peripheral vein at the forearm.The contrast agent was followed by saline bolus chaser.A bolus tracking tool was used to trigger the start of arterial acquisition once the region of interest (ROI) in the proximal descending aorta exceeded 125 HU.